Research Projects

Obesity has become a major public health crisis in India, with the overall prevalence now estimated at 40.3% among adults, and rates even higher in urban areas (44.17%) and southern regions (46.51%) of the country (Murali et al.,2021).This epidemic is driven by shifts in dietary patterns and eating behaviours such as reduced chew count and higher calorie intake, which contribute to rapid food consumption, diminished satiety, and progressive weight gain. (Hidaka et al.,2023). To address these challenges, advances in 3D food printing technology offer a novel, structural approach. By manipulating infill density and patterns during 3D printing, foods can be engineered with tailored textures and caloric densities (Lin et al.,2020). Thus, integrating the psychological principle in the eating behaviour modulation strategy with the help of emerging texturization printing technique gains importance in the obesity management.The use of ingredients such as jamun fruit powder, finger millet, coconut powder and palm sugar can enhance the nutritional profile of Indian ethnic functional foods, supporting both obesity and diabetes management.Jamun fruit helps prevent obesity and diabetes by improving insulin sensitivity, reducing blood sugar levels, and modulating gut microbiota, which supports metabolic health (Rizvi et al.,2020). Finger millet and palm sugar are particularly beneficial due to its low glycemic index, and ability to slow glucose absorption and improve insulin sensitivity (Agarwal et al., 2023). With these ingredient, our aim is to produce 3D printed functional Jamun-finger millet dumplings (ie. Ragi kolukkatai) with customized textural attributes. Thus, initial study will be on the pretreatment and printability studies of Jamun-finger millet mix. Then the study will be focused on the effect of printing design parameters and post-processing parameters on the product’s sensorial attributes. Finally, the effect of selected 3D printed food on Obesity will be evaluated in in-vitro and in-vivo conditions. AI-ML models can predict post-processing outcomes such as color change, weight reduction, shrinkage percentage and hardness across various infill percentages and geometric designs by analyzing experimental datasets that capture the relationships between printing parameters and resulting food properties after processing. This allows manufacturers to digitally fine-tune infill patterns and other variables to achieve desired textures and calorie profiles without conducting numerous physical trials, significantly reducing development time and resource expenditure (Chen et al.,2023). This multidisciplinary strategy leverages food engineering, nutritional science, and data-driven automation to deliver innovative solutions for obesity intervention in India without sacrificing tradition or consumer satisfaction.